Thursday, August 8, 2013

Starting Small: Set Up a Hadoop Compute Cluster Using Raspberry Pis

Hadoop is a big data computing framework that generally refers to the main
components: the core, HDFS,
and MapReduce. There
are several other projects under the umbrella as well. For more information,
see this interview
with Cloudera
CSO Mike Olson.

The Pi is a small, inexpensive ($39) ARM
based computer. It is meant primarily as an educational tool.

Is Hadoop on the Pi practical?

Nope! Compute performance is horrendous.

Then why?

It's a great learning opportunity to work with Hadoop and multiple nodes.
It's also cool to be able to put your "compute cluster" in a lunch box.
In reality though, this article is much more about setting up your first
Hadoop compute cluster than it is about the Pi.

Could I use this guide to setup a non-Raspberry Pi based (Real) Hadoop
Cluster?

Absolutely, please do. I'll make notes to that effect throughout.

I've been wanting to do this anyhow, let's get started!

Yeah, that's what I said.

Hardware Used

3x Raspberry Pis

1x 110v Power Splitter

1x 4 port 10/100 Switch

1x Powered 7 port USB hub

3x 2' Cat 5 cables

3x 2' USB "power" cables

3x Class 10 16GB SDHC Cards

Total cost: about $170 bucks. I picked it all up at my local Microcenter,
including the Pis!

Pi Hadoop Cluster;
caution: may be slow.

If you would like a a high performance Hadoop cluster just pick one of these
up on the way home:

This will cost at
least $59.95. Maybe more. Probably more.

Raspberry Pi Preparation

We'll do the master node by itself first so that this guide can be used
for a single or multi-node setup. Note: On a non-Pi
installation skip to Networking Preparation (though you may want to update
your OS manually).

Initial Config

Download the "Raspbian wheezy" image from here
and follow these
directions to set up your first SD card. Soft-float isn't necessary.

Hook up a monitor and keyboard and start up the device by connecting
up the USB power.

When the Raspberry Pi config tool launches, change the keyboard layout
first. If you set passwords, etc. with the wrong KB layout you may have
a hard time with that later.

Change the timezone, then the locale. (Language, etc.)

Change the default user password

Configure the Pi to disable the X environment on boot.
(boot_behavior->straight to desktop->no)

Update The OS

Note: It is assumed from henceforth you are connecting
to the Pi or your OS via SSH.
You can continue on the direct terminal if you like until we get to
multiple nodes.

Log on to your Pi using the Raspberry user with the password you set
earlier.

To pull the newest sources, execute sudo apt-get update

To pull upgrades for the OS and packages, execute sudo apt-get upgrade

Reboot the pi. (sudo reboot)

Split the Memory/Overclock

After logging back into the Pi, execute sudo raspi-config to bring up
the Rasperry Pi config tool.

Select Option 8, "Advanced Options"

Select Option A3, "Memory Split"

When prompted for how much memory the GPU should have, enter the
minimum, "16" and hit enter.

(If you would like to overclock) Select Option 7, "Overclock", hit
"OK" to the warning, and select what speed you would like.

On the main menu, select "finish" and hit "Yes" to reboot.

Networking Preparation

Each Hadoop node must have a unique name and static IP. I'll be
following the Debian instructions since the Rasbian build is based on Debian.
If you're running another distro follow the instructions for that. (Redhat,
CentOS, Ubuntu)

First we need to change the machine hostname by editing /etc/hostname

sudo nano /etc/hostname

Note: I'll be using nano throughout the article; I'm sure
some of you will be substituting vi instead. :)
Change the hostname to what you would like and save the file; I used "Node
1" throughout this document. If you're setting up the second node
make sure to use a different hostname.

domain=domain suffix for this machine
search=appends when FQDN not specified
nameserver=list DNS servers in order of precedence

We could restart networking etc, but for the sake of simplicity we'll bounce
the box:

sudo reboot

Install Java

If you're going for performance you can install the SunJDK, but getting
that to work requires a bit of extra effort. Since we're on the Pi and
performance isn't our goal, I'll be using OpenJDK which installs easily.
If you're installing on a "real" machine/VM, you may want to diverge a bit
here and go for the real deal (Probably Java ver 6).
After connecting to the new IP via SSH again, execute:

sudo apt-get install openjdk-7-jdk

Ensure that 7 is the version you want. If not substitute the package
accordingly.

Create and Config Hadoop User and Groups

Create the hadoop group:

sudo addgroup hadoop

Create hadoop user "hduser" and place it in the hadoop group; make sure you
remember the password!

sudo adduser --ingroup hadoop hduser

Give hduser the ability to sudo:

sudo adduser hduser sudo

Now logout:

logout

Log back in as the newly created hduser. From hence forth
everything will be executed as that user.

Setup SSH Certs

SSH is used between Hadoop nodes and services (even on the same node) to
coordinate work. These SSH sessions will be run as the Hadoop user, "hduser"
that we created earlier. We need to generate a keypair to use for EACH node.
Let's do this one first:

Create the certs for use with ssh:

ssh-keygen -t rsa -P ""

When prompted, save the key to the default directory (will be /home/USERNAME/.ssh/id_rsa).
If done correctly you will be shown the fingerprint and randomart image.

Copy the public key into the user's authorized keys store (~ represents the
home directory of the current user, >> appends to that file to
preserve any keys already there)

cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

SSH to localhost and add to the list of known hosts.

ssh localhost

When prompted, type "yes" to add to the list of known hosts.

Download/Install Hadoop!

Note: As of this writing, the newest version of the 1.x
branch (which we're covering) is 1.2. (Ahem, 1.2.1, they're quick) You should check to see what the
newest stable release is and change the download link below accordingly.
Versions and mirrors can be found here.

Bounce the box (you could actually just log off/on, but what the hell eh?):

sudo reboot

Configure Hadoop!

(Finally!) It's time to setup Hadoop on the machine. With these steps we'll
set up a single node and we'll discuss multiple node configuration
afterward.
First let's ensure your Hadoop install is in place correctly. After logging
in as hduser, execute:

hadoop version

you should see something like: (differs depending on version)

Hadoop 1.2.0
Subversion https://svn.apache.org/repos/asf/hadoop/common/branches/branch-1.2 -r 1479473
Compiled by hortonfo on Mon May 6 06:59:37 UTC 2013
From source with checksum 2e0dac51ede113c1f2ca8e7d82fb3405
This command was run using /usr/local/hadoop/hadoop-core-1.2.0.jar

Good. Now let's configure. First up we'll set up the environment file
that defines some overall runtime parameters:

nano /usr/local/hadoop/conf/hadoop-env.sh

Add the following lines to set the Java Home parameter (specifies where Java
is) and how much memory the Hadoop processes are allowed to use. There
should be sample lines in your existing hadoop-env.sh that you can just
un-comment and modify. Note: If you're using a "real" machine rather than a Pi,
change the heapsize to a number appropriate to your machine (physical mem -
(OS + caching overhead)) and ensure you have the right directory for
JAVA_HOME because it will likely be different. If you have a Raspberry Pi version "A" rather than "B", you'll heapsize will need to be much lower since the RAM is halved.

Now let's configure core-site.xml. This file defines critical operational
parameters for a Hadoop site. Note: You'll notice we're using "localhost" in the
configuration. That will change when we go to multi-node, but we'll leave it
as localhost for a demonstration of an exportable local only configuration.

nano /usr/local/hadoop/conf/core-site.xml

Add the following lines. If there is already information in the file make
sure you respect XML
format rules. The "description" field is not required, but I've
included it to help this tutorial make sense.

It's time to configure the mapred-site.xml. This file
determines where the mapreduce job tracker(s) run. Again, we're using
localhost for now and we'll change it when we go to multi-node in a bit.

nano /usr/local/hadoop/conf/mapred-site.xml

Add the following lines:

<configuration>
<property>
<name>mapred.job.tracker</name>
<value>localhost:54311</value>
<description>The host and port that the MapReduce job tracker runs
at. If "local", then jobs are run in-process as a single map
and reduce task.
</description>
</property>
</configuration>

Now on to hdfs-site.xml. This file configures the HDFS parameters.

nano /usr/local/hadoop/conf/hdfs-site.xml

Add the following lines:
Note: The dfs.replication value sets how many copies of any given file
should exist in the configured HDFS site. We'll do 1 for now since there is
only one node, but again when we configure multiple nodes we'll change this.

<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
<description>Default block replication.
The actual number of replications can be specified when the file is created.
The default is used if replication is not specified in create time.
</description>
</property>
</configuration>

If there are any missing processes, you'll need to review logs. By default,
logs are located in:

/usr/local/hadoop/logs

There are two types of log files: .out and .log. .out files detail process
information while .log files are the logging output from the process.
Generally .log files are used for troubleshooting. The logfile name standard
is:

hadoop-(username)-(processtype)-(machinename).log

i.e.

hadoop-hduser-jobtracker-node1.log

In this, like many other articles, we'll use the included wordcount example.
For the wordcount example you will need plain text large enough to be
interesting. For this purpose, you can download plain text books from Project
Gutenberg.

After downloading 1 or more books (make sure you selected "Plain Text UTF-8"
format) copy them to the local filesystem on your Hadoop node using SSH (via
SCP or similar). In
my example I've copied the books to /tmp/books.

Now let's copy the books onto the HDFS filesystem by using the Hadoop dfs
command.

The results from the run, if you're curious, should be in the last part of
the command you executed earlier. (/fs/hduser/books-output in our case) Update: to check the output use the hadoop dfs command a la:

Congrats! You just ran your first Hadoop job, welcome to the world of
(tiny)big data!

Multiple Nodes

Hadoop has been successfully run on thousands of nodes; this is where we
derive the power of the platform. The first step to getting to many nodes is
getting to 2, so let's do that.

Set up the Secondary Pi

I'll cover cloning a node for the third Pi, so for this one we'll assume
you've set up another Pi the same as the first using the instructions above.
After that, we just need make appropriate changes to the configuration
files. Important Note: I'll be referring to the nodes as
Node 1 and Node 2 and making the assumption that you have named them that.
Feel free to use different names, just make sure you substitute them in
below. Name resolution will be an issue; we'll touch on that below.

Node 1 will run:

NameNode

Secondary NameNode (In a large production cluster this would be
somewhere else)

DataNode

JobTracker (In a large production cluster this would be somewhere
else)

TaskTracker

Node 2 will run:

DataNode

TaskTracker

Normally this is where I planned on writing up an overview of the
different pieces of Hadoop, but upon researching I found an article that
exceeded what I planned to write it by such a magnitude I figured why
bother, I'll just link
it here. Excellent post by Brad
Hedlund, be sure to check it out.
On Node 1, edit masters

nano /usr/local/hadoop/conf/masters

remove "localhost" and add the first node FQDN and save the file:

Node1.domain.ext

Note that conf/masters does NOT determine which node holds "master" roles,
i.e. NameNode, JobTracker, etc. It only defines which node will attempt to
contact the nodes in slaves (below) to initiate start-up.
On Node 1, edit slaves

nano /usr/local/hadoop/conf/slaves

remove "localhost" add the internal FQDN of all nodes in the cluster and
save the file:

Node1.domain.ext
Node2.domain.ext

On ALL (both) cluster members edit core-site.xml

nano /usr/local/hadoop/conf/core-site.xml

and change the fs.default.name value to reflect the location of the
NameNode:

and change the dfs.replication value to reflect the location of the
JobTracker:

<property>
<name>dfs.replication</name>
<value>2</value>
</property>

The dfs.replication value determines how
many copies of any given piece of data will exist in the HDFS
site. By default this is set to 3 which is optimal for many small->medium
clusters. In this case we set it to the number of nodes we'll have right
now, 2.

Now we need to ensure the master node can talk to the slave node(s) by
adding the master key to the authorized keys file on each node (just 1 for
now)
On Node 1, cat your public key and copy it to the
clipboard or some other medium for transfer to the slave node

cat ~/.ssh/id_rsa.pub

On Node 2

nano ~/.ssh/authorized_keys

and paste in the key from the master and save the
file to allow it to take commands.

Name Lookup

The Hadoop nodes should be able to resolve the names of the nodes in the
cluster. To accomplish this we have two options: the right way and the quick
way.

The right way: DNS

The best way to enable name lookup works correctly is by adding the nodes
and their IPs to your internal DNS. Not only does this facilitate standard
Hadoop operation, but it makes it possible to browse around the Hadoop
status web pages (more on that below) from a non-node member, which I
suspect you'll want to do. Those sites are automatically created by Hadoop
all the hyperlinks use the node DNS names, so you need to be able to resolve
those names from a "client" machine. This can scale too; with the right tool
set it's very easy to automate DNS updates when spinning up a node.

To do this option, add your Hadoop hostnames to the DNS zone they reside in.

The quick way: HOSTS

If you don't have access to update your internal DNS zone, you'll need to
use the hosts files on the nodes. This will allow the nodes to talk to each
other via name. There are scenarios where you may want to automate the
updating of node based HOSTS files for performance or fault tolerance
reasons as well, but I won't go into that here.

To use this option do the following:

On ALL (both) cluster members edit /etc/hosts

nano /etc/hosts

and add each of your nodes with the appropriate IP addresses (mine are only
examples)

192.168.1.40 node1
192.168.1.41 node2

Now that the configuration is done we need to format the HDFS filesystem. Note:
THIS IS DESTRUCTIVE. Anything currently on your single node
Hadoop system will be deleted.
On Node 1

bin/hadoop namenode -format

When that is done, let's start the cluster:

cd /usr/local/hadoop
bin/start-all.sh

Just as before, use the jps command to check processes, but this time on
both nodes.
On Node 1

jps

You should see the following processes: (ignore the number in front, that's
a process ID)

You should see the following processes: (ignore the number in front, that's
a process ID)

6365 TaskTracker
7248 Jps
6279 DataNode

If there are problems review the logs. (for detailed location see
reference above) Note that by default each node contains its own logs.

Now let's run the wordcount example in a cluster. To facilitate this you'll
need enough data to split, so when downloading UTF-8 books from Project
Gutenberg get at least 6 very long books (that should do with default
settings).

Copy them to the local filesystem on your master Hadoop node using SSH (via
SCP or similar). In
my example I've copied the books to /tmp/books.

Now let's copy the books onto the HDFS filesystem by using the Hadoop dfs
command. This will automatically replicate data to all nodes.

Monitoring HDFS/Jobs

Hadoop includes a great set of management web sites that will allow you to
monitor jobs, check logfiles, browse the filesystem, etc. Let's take a
moment to examine the three sites.

JobTracker
By default, the job tracker site can be found at http://(jobtrackernodename.domain.ext):50030/jobtracker.jsp
; i.e. http://node1.company.com:50030/jobtracker.jsp

On the Job Tracker you can see information regarding the cluster, running
map and reduce tasks, node status, running and completed jobs, and you can
also drill into specific node and task status.

DFSHealth
By default, the DFSHealth site can be found at http://(NameNodename.domain.ext):50070/dfshealth.jsp
; i.e. http://node1.company.com:50070/dfshealth.jsp

The DFSHealth site allows you to browse the HDFS system, view nodes, look at
space consumption, and check NameNode logs.

TaskTracker
By default, the task tracker site can be found at http://(nodename.domain.ext):50060/tasktracker.jsp
; i.e. http://node2.company.com:50060/tasktracker.jsp

The Task Tracker site runs on each node that can run a task to provide more
detailed information about what task is running on that node at the time.

Cloning a Node and Adding it to the Cluster

Now we'll explore how to get our third node into the cluster the quickest
way possible: cloning the drive. This will cover, at a high level, the SD
card cloning process for the Pi. If you're using a real machine replace the
drive cloning steps with your favorite (*cough*Clonzilla*cough*) cloning
software, or copy the virtual drive if you are using VMs.

You should always clone a slave node unless you intend on making a new
cluster. To this end, do the following:

Shut down Node 2 (sudo init 0) and remove
the SD card.

Insert the SD card into a PC to so we can clone it; I'm using a
Windows 7 machine so if you're using Linux we'll diverge here. (I'll
meet you at the pass!)

Remove the Node 2 SD card and place it back into Node 2. Do not power
up yet.

Insert a new SD card of the same size to your imaging PC.

Delete any partitions on the card to ensure error-free imaging. I use
Diskpart , Select Disk x , Clean , exit
where "x" is the disk number. Use list disk to find it...
don't screw up or you'll wipe out your HDD!

Image the new SD card with the image from Node 2 and remove it from
the PC when complete.

Insert the newly cloned SD card to Node 3. Power up that node and
leave Node 2 off for now or the IPs will conflict.

Log into the new node (same IP as the cloned node) via ssh with the hduser
(Hadoop user) account and perform the following tasks:

Change the hostname:

sudo nano /etc/hostname

For this example change "Node2" to "Node3"
and save the file.

Change the IP address:

sudo nano /etc/network/interfaces

For this example change "192.168.1.41" to "192.168.1.42"
and save the file. Switch to your own IP address range if necessary.

If you're using HOSTS for name resolution, add Node 3 with the proper IP
address to the hosts file

sudo nano /etc/hosts

Repeat this on the master node and for the sake of
completeness the other node(s) should probably be updated as well. In a
large scale deployment this would all be automated.
If you are using DNS, make sure to add the Node3 entry to your zone.

Reboot Node 3 to enact the IP and hostname changes

sudo reboot

Power up Node 2 again at this time if you had it powered down. Log back into
Node 3 (now with the new IP!) with the hduser account via SSH.

Generate a new key:

ssh-keygen -t rsa -P ""

Copy the key to its own trusted store:

cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

If you aren't already, log into Node1 (Master) as hduser via SSH and do the
following:Add Node 3 to the slaves list

nano /usr/local/hadoop/conf/slaves

SSH to Node 3 to add it to known hosts

ssh rasdoop3

When prompted re: continue connecting type yes

An ALL nodes edit hdfs-site.xml

nano /usr/local/hadoop/conf/hdfs-site.xml

Change it to replicate to 3 nodes. Note if you add more nodes to the cluster
after this you would most likely NOT increase this above 3. It's not 1:1
that we're going for, it's the number of replicas we want in the entire
cluster. Once you're running a cluster with > 3 nodes you'll generally
know if you want dfs.replication set to more than 3.

<configuration>
<property>
<name>dfs.replication</name>
<value>3</value>
<description>Default block replication.
The actual number of replications can be specified when the file is created.
The default is used if replication is not specified in create time.
</description>
</property>
</configuration>

Now we need to format the filesystem. It is possible, but slightly more
complicated, to clone/add a node without re-formatting the whole HDFS
cluster, but that's a topic for another blog post. Since we have virtually
no data here, we'll address all concerns by wiping clean:
On Node 1:

cd /usr/local/hadoop
bin/hadoop namenode -format

Now we should be able to start up all three nodes. Let's give it a shot!
On Node 1:

bin/start-all.sh

Note: If you have issues starting up the Datanodes you may
need to delete the sub-directories under the directory listed in
core-site.xml as "hadoop.tmp.dir" and then re-format again (see
above).

As for startup troubleshooting and running the wordcount example, copy out
the instructions above after the initial cluster setup. There should be
nothing different from running with two nodes save the fact that you may
need more books to get it working on all 3 nodes.

Postmortem/Additional Reading

You did it! You've now got the experience of setting up a Hadoop cluster
under your belt. There's a ton of directions to go from here; this
relatively new tech is changing the way a lot of companies view data and
business intelligence.

There is so much great community content out there; here's just a small list
of the references I used and some additional reading.

Toby: I'm running into a problem where the job tracker and task tracker start and then abort with the following error:FATAL org.apache.hadoop.mapred.JobTracker: java.lang.IllegalArgumentException: Does not contain a valid host:port authority: local at org.apache.hadoop.net.NetUtils.createSocketAddr(NetUtils.java:164) at org.apache.hadoop.net.NetUtils.createSocketAddr(NetUtils.java:130) at org.apache.hadoop.mapred.JobTracker.getAddress(JobTracker.java:2131) at org.apache.hadoop.mapred.JobTracker.(JobTracker.java:1869) at org.apache.hadoop.mapred.JobTracker.(JobTracker.java:1689) at org.apache.hadoop.mapred.JobTracker.(JobTracker.java:1683) at org.apache.hadoop.mapred.JobTracker.startTracker(JobTracker.java:320) at org.apache.hadoop.mapred.JobTracker.startTracker(JobTracker.java:311) at org.apache.hadoop.mapred.JobTracker.startTracker(JobTracker.java:306) at org.apache.hadoop.mapred.JobTracker.main(JobTracker.java:4710)

Task tracker is similar.

I've searched several places but haven't found anything that resolves the issue. I'm still at the single node phase of the basic setup.

Yes, that is because of the version of Java you are using. I'm guessing this applies to you. Here is a huge discussion on the topic... there are a few options out there Java wise. For a proof of concept, you could try switching over to OpenJDK. Good luck!

Toby - Thanks -- I fixed it, but I'm not sure how. Best guess is that it was a dos2unix type issue since I had cut and pasted the config file information directly from this site, and the functional version looks identical to what's posted here.I have two nodes to work with at this time. I had both run the wordcount example successfully, and now I am working on getting the master-slave version working.

Excellent! I'm glad you both got it working. I hadn't thought of that issue Ken, that makes sense. I suppose here is the potential to introduce an text encoding issue with copy/paste depending on what SSH client you use. Good job getting around that.. I'm sure that information will be useful to other folks setting it up as well.

I just happen to have 3 raspberry Pi around, and haven't found anything interesting enough to do with them. I also happen to have just signed up for udacity's hadoop class. This is what I am going to do with my 3 Pis. Thanks!

Assuming you want to use Hadoop to serve up a JSP I'd direct you to this answer at Stack Overflow. Unless you really need to I would advise against using the built in Jetty to serve up non-Hadoop standard content as it goes against the way Hadoop attempts to load balance across nodes. That said, it could be done. If you do want to go down that path you'll most likely need to do it in code and this would get you started.

I really wanted this tutorial to work. I've gone through it 3 times and continue to get this error after the command to copy the books onto the HDFS filesystem by using the Hadoop dfs command.

13/12/10 12:45:21 WARN hdfs.DFSClient: DataStreamer Exception: org.apache.hadoop.ipc.RemoteException: java.io.IOException: File /fs/hduser/books/kjv.txt could only be replicated to 0 nodes, instead of 1 at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.getAdditionalBlock(FSNamesystem.java:1920) at org.apache.hadoop.hdfs.server.namenode.NameNode.addBlock(NameNode.java:783) at ... stack trace is much bigger but comments on blog is limited.

- If you have a firewall enabled, disable it on each node. This can be done temporarily with the command service iptables stop. (Debian, Rasbian if applicable) If that works, then you'll need to add firewall exceptions if you wish to leave it enabled. - Make sure the fs.default.name entry in core-site.xml is correct on your slave nodes; they must be pointing at the (HDFS)master. - I'm sure you've done this, but make sure you format HDFS after all nodes are added. Try /usr/local/hadoop/bin/hadoop namenode -format- Try starting all nodes separately and watching for errors. See this Stack post for more info.

I put this on the shelf for a while to come back to it fresh. I re-installed the os on the SD card to start from scratch. There is no firewall. I've run into the exact same issue every time. I'm using Hadoop 1.2.1.

Hey Brendon! Good question; that may be due to a DNS lookup error for node1. Try adding the node1 record to your preferred dns servers (listed in /etc/resolv.conf) or put the correct IP address into /etc/hosts on each node. Make sure you add entries for all nodes in the cluster.

The second error is most likely related to the first so hopefully that takes care of both.

Then we try to copy a text-file to hadoop using hdfs -copyFromlocal, but it keeps complaining that it cannot find the provided folder (the target folder, in your example )fs/hduser/books). How would you checkt if the folder exists ( it should be hdfs i think). We could find it, but maybe our search was incorrect?

How do i traverse the hdfs? How can i make sure that the folder is created?

Stupendous? I don't know if you can be *too* stupendous. :) Keep in mind this stuff can be difficult, but eventually we'll get it.

To traverse/manipulate the filesystem, you need the "hadoop dfs"(or just fs) command.(assuming at least the data node(s) are running) The best syntax guide I've found is here. So if for example you wanted to check the /fs/hduser path for the books directory, you would type: hadoop dfs -ls /fs/hduser Then if you need to create it the other commands are generally just like the *nix equivalents. Note, however, that hadoop dfs on the Raspberry Pis can seem very slow sometimes.

@Brandon:Excellent! Glad to hear it; I must confess this project has been one of my favorites.

thank you very much for your answer. We've been trying this several times, bt we always get: 'No such file or directory'.Even if i try to make a new directory:e.g.,hadoop dfs -mkdir /user/hadoop/dir1it keeps reporting the no-such-file error :/

- Ensure your local firewall is either disabled or configured correctly. On Debian/Raspbian you can temporarily disable it with service iptables stop- Shut everything down and startup one at a time, checking for errors after each one. The order should be:

After that you can start the trackers with /usr/local/hadoop/bin/start-mapred.sh, but I would bet you'll find the error prior to that.

(If you're running multiple nodes, which it looks like you aren't but just in case)

- Try replacing "localhost" in most cases with the actual node name. For example, fs.default.name in /usr/local/hadoop/conf/core-site.xml should point to the hostname of the name node. - Check your DNS/name lookup configuration; if using the /etc/hosts file each node must have all nodes in the cluster (including itself) in the file.

After you name/data node(s) are coming up right you will have to make sure you format HDFS with: /usr/local/hadoop/bin/hadoop namenode -format

14/02/16 00:17:08 INFO namenode.FSEditLog: Number of transactions: 1 Total time for transactions(ms): 1 Number of transactions batched in Syncs: 0 Number of syncs: 0 SyncTimes(ms): 0 14/02/16 00:17:08 WARN namenode.FSNamesystem: Not able to place enough replicas, still in need of 1 to reach 1Not able to place enough replicas14/02/16 00:17:08 ERROR security.UserGroupInformation: PriviledgedActionException as:hduser cause:java.io.IOException: File /app/hadoop/tmp/mapred/system/jobtracker.info could only be replicated to 0 nodes, instead of 114/02/16 00:17:08 INFO ipc.Server: IPC Server handler 5 on 54310, call addBlock(/app/hadoop/tmp/mapred/system/jobtracker.info, DFSClient_NONMAPREDUCE_-502449137_1, null) from 127.0.0.1:36898: error: java.io.IOException: File /app/hadoop/tmp/mapred/system/jobtracker.info could only be replicated to 0 nodes, instead of 1java.io.IOException: File /app/hadoop/tmp/mapred/system/jobtracker.info could only be replicated to 0 nodes, instead of 1

A couple pointers...-hdfs://localhost:54310 is correct; the core-site configuration file needs a protocol definition preceding the host/port. -the hadoop fsck / didn't look too bad! If your datanode wasn't running that command would return an error rather than any results, so that's a good sign. - This error message: INFO org.apache.hadoop.ipc.RPC: Server at rasdoop1/192.168.1.40:54310 not available yet, Zzzzz... is "normal", especially in the Raspberry Pi setup since the machines are so slow. As long as you see something like: INFO org.apache.hadoop.hdfs.server.datanode.DataNode: Registered FSDatasetStatusMBean afterward it's no problem. - to confirm it's running, what do you see when you execute jps? You should see:

Looks like you made some progress; the job tracker is connecting now. I'm a little curious about the hdfs://vmware reference; could you post the contents of your conf/core-site.xml? Is the only item in your hdfs-site.xml the dfs.replication value or are there additional settings? Also, what does your /etc/hosts look like? There have been issues where the localhost entry isn't simply:

127.0.0.1 localhost

Oh, just so I'm certain too.. you are running just one node right now correct?

hadoop.tmp.dir /app/hadoop/tmp A base for other temporary directories.

fs.default.name hdfs://vmware:54310 The name of the default file system. A URI whose scheme and authority determine the FileSystem implementation. The uri's scheme determines the config property (fs.SCHEME.impl) naming the FileSystem implementation class.The url's authority is used to determine the host, port, etc. for a filesystem.

the dfs.replication is the only info in the hdfs-site.xml and is set to 1.

hdfs-site.xml

dfs.replication 1 Default block replication. The actual number of replications can be specified when the file is created. The default is used if replication is not specified in create time.

my /etc/hosts file looks like127.0.0.1 localhost192.168.11.119 vmware

I also make sure that my hostname is correctly setup to 'vmware' in /etc/hostname

And I do confirm I am running just one node (with all the 'vmware' machine hosting all the processes for the time being.

2.2 will require some additional and different settings. I'm hoping to create a 2.2 version of this article soon, but for the time being this article is wonderful. Most of it (the Hadoop specific portions) still applies to the Pi. There are additional needed environment variables (.bashrc) and some of the default ports have changed, so pay special attention to those parts. If you want some info with a little more "meat", give hortonworks a try, theirs is always great.

If you need to check which ports are open to be certain, you can always use netstat -np.

Hi Toby.What a great post, far more clear and concise than some I have been trying to follow!I have everything place with 2 PIs, jobs start on both, and when i load in books, all seems good.However when I run the word count example, the HealthCheck page only ever shows 1 node, and the time it takes to run increases with more books...so i am assuming node2 never kicks in. I am loading 20 Books (26.6MB) ,should that be enough to kick the 2nd node into action?all config files seem ok, and both nodes can talk to each other.Any advice would be greatly appreciated.CheersRob

I have hit a snag at the last hurdle and was wondering if you had any thoughts on how to solve the problem. I have installed and configured everything and all seems ok until I go to run the Map Reduce example. I get the following :

Thanks for the kind words! Both nodes won't kick in unless the data set is large enough that it can be split between the two. To get all nodes working in my case I had to start jobs using several of the largest books I can find, so load up some "War and Peace" (and a few others) and give it another shot!

P.S. Winter is coming. :)

@Paul

Looks like there may be a couple problems, but first and foremost name resolution (0.0.0.0). I believe the resource manger settings are covered under /conf/yarn-site.xml in 2.3; can you make sure all the ".address" values are correct in that file? If using hostnames, make sure you can resolve the listed names correctly. If not using DNS you may need to add them to your /etc/hosts file on all nodes. Good luck!

what a fantastic blog! I had already purchased the "Raspberry Pi Super Cluster" - but your blog has far better descriptions for the Hadoop section. :-)

I followed your blog and have a question. Everything 'seems' to work for me in that when i run the startup script I can see the correct jps processes working on each of my nodes. I have one master and 2 workers. So on the workers I have:DataNode; andTaskTracker;

On my master I have:JobTracker;NameNode;TaskTracker;DataNode; andSecondaryNameNode.

However on multiple books being processed I had an error with java not having enough 'Heap' - I have mine set as 272 as your tutorial suggests.

When I do a wordcount on just a small book it takes maybe 10 minutes to complete! Do you get this?What Raspberry Pi's did you use? Revision A or B? I'm using 'A'.

I also noticed on my setup on the NameNode JSP page it only every shows 1 live node! And the %cpu for my java processes on the slave machine never go above a 5% - do you get this?

Final question! Honest! :-), I get warnings about a 'snappy' java library not loaded, so the system uses a built in library, which i guess is down to me using the OpenJDK. Does this make much difference?

what a fantastic blog! I had already purchased the "Raspberry Pi Super Cluster" - but your blog has far better descriptions for the Hadoop section. :-)

I followed your blog and have a question. Everything 'seems' to work for me in that when i run the startup script I can see the correct jps processes working on each of my nodes. I have one master and 2 workers. So on the workers I have:DataNode; andTaskTracker;

On my master I have:JobTracker;NameNode;TaskTracker;DataNode; andSecondaryNameNode.

However on multiple books being processed I had an error with java not having enough 'Heap' - I have mine set as 272 as your tutorial suggests.

When I do a wordcount on just a small book it takes maybe 10 minutes to complete! Do you get this?What Raspberry Pi's did you use? Revision A or B? I'm using 'A'.

I also noticed on my setup on the NameNode JSP page it only every shows 1 live node! And the %cpu for my java processes on the slave machine never go above a 5% - do you get this?

Final question! Honest! :-), I get warnings about a 'snappy' java library not loaded, so the system uses a built in library, which i guess is down to me using the OpenJDK. Does this make much difference?

Thank you very much, I appreciate the compliment. First issue is the heapsize with the A model. I am using B, and a heapsize of 272 will be too much for an A. I would bring it all the way down to 142 or so in your situation, and I've updated the article to clarify (Thanks, I didn't consider that!). On my Pis I it takes a few minutes to complete; I'm guessing with less RAM it will be even slower. Also, I think you may have a cluster issue that we'll discuss below. That said, this definitely is not for performance but rather learning Hadoop easily and cheaply. Now that you've done it here you can carry it forward into a production environment with 16-way servers with gigs and gigs of ram and SSDs as far as the eye can see. :)

As for the NameNode JSP page(assuming you're talking 50070:dfshealth.jsp), it does seem like something is wrong there. Double check your config files under the "Multiple Nodes" section; something may be wrong there. They should be reporting and working harder than that. Additionally, check the logs on your worker nodes after performing a start-all.sh. Feel free to post any errors here so we can get to the bottom of it.

The snappy warning is not a big deal; there are faster libs than OpenJDK but as stated earlier these aren't going to be fast either way.

Hey Donald! You're in luck, I wrote this article with that in mind. :) There is no reason you can't follow the article top to bottom with VMs as well with a couple minor differences:

- Some of the OS/tool syntax will differ depending on the distro. In your case, for example, APT will be replaced with YUM since you're running CentOS. - The JVM you will probably end up using should be Oracle. (see this) - Obviously the hardware/firmware guidance will differ, such as export HADOOP_HEAPSIZE=272 should be changed to more closely reflect how much memory your machine has. For example, if your VM has in around 1GB of RAM allocated you could set the heapsize to 800 or so.

First of all, thank you for this great blog!I have a question. I installed Hadoop 2.2.0 on my Raspberry Pi model B. I also completed the configuation including the yarn-site.xml. When I finally run the command start-dfs.sh the following line is shown:"localhost: Server VM is only supported on ARMv7+ VFP".

I encountered a problem at jps. My jps only can detect itself evendo I have enable start-all.sh. I am using hadoop version 1.2.1 and java 1.7.0_65 openjdk. Can it be the java version I'm used can't detect other process? thanks.

Hi Toby - Thanks for your help last year - We've got a poster at ACM SIGCSE 2015 next week "Raspberry HadooPI: A Low-Cost, Hands-On Laboratory in Big Data and Analytics ". That works in large part because of the getting started help this post provided for us. I mounted the whole mobile classroom in a briefcase. There's a couple photos over on https://cs498.blogspot.com/, if you'd like to take a look, but basically: THANKS!-- Ken

So Toby, I feel like this is a dumb question since no one else asked but...after you run the word count example on the single pi...I don't understand what you mean by, "The results from the run, if you're curious, should be in the last part of the command you executed earlier. (/fs/hduser/books-output in our case)"? What do I do to see the wordcount in the text document? Thanks!

Interesting !!!I don't understand what you mean by, "The results from the run, if you're curious, should be in the last part of the command you executed earlier. (/fs/hduser/books-output in our case)"? What do I do to see the word count in the text document? Thanks!